A Comparative Study of Various Database Scan Techniques for Frequent Pattern Generation

摘要

There are lots of data mining tasks such as association rule, clustering, classification, regression and others. Among these tasks association rule mining is most prominent. One of the most popular approaches to find frequent item set in a given transactional dataset is Association rule mining. Frequent pattern mining is one of the most important tasks for discovering useful meaningful patterns from large collection of data. This paper explores the various database scan techniques for frequent pattern generation. These preprocessing techniques are very useful and important for reducing database scan time and space. There are lots of preprocessing techniques are available some of them discussed in this paper such as FP Tree, CP Tree, CFP Tree, K Map, Hash Tree, FP Growth Tree, COFI Tree, CT-PRO Tree. This paper also focuses on the comparative analysis of various compact database scan generation techniques on the basis of some parameters.